A paradigm shift from static spectrum allocation to dynamic spectrum access (DSA) is becoming a reality due to the recent advances in cognitive radio, wide band spectrum sensing, and network aware real--time spectrum access. It is believed that DSA will allow wireless service providers (WSPs) the opportunity to dynamically access spectrum bands as and when they need it. Moreover, due to the presence of multiple WSPs in a region, it is anticipated that dynamic service pricing would be offered that will allow the end-users to move from long-term service contracts to more flexible short-term service models. In this research, we develop a unified economic framework to analyze the trading system comprising two components: i) spectrum owner--WSPs interactions with regard to dynamic spectrum allocation, and ii) WSP--end-users interactions with regard to dynamic service pricing. For spectrum owner--WSPs interaction, we investigate various auction mechanisms for finding bidding strategies of WSPs and revenue generated by the spectrum owner. We show that sequential bidding provides better result than the concurrent bidding when WSPs are constrained to at most single unit allocation. On the other hand, when the bidders request for multiple units, (i.e., they are not restricted by allocation constraints) synchronous auction mechanism proves to be beneficial than asynchronous auctions. In this regard, we propose a winner determination sealed-bid knapsack auction mechanism that dynamically allocates spectrum to the WSPs based on their bids. As far as dynamic service pricing is concerned, we use game theory to capture the conflict of interest between WSPs and end--users, both of whom try to maximize their respective net utilities. We deviate from the traditional per--service static pricing towards a more dynamic model where the WSPs might change the price of a service almost on a session by session basis. Users, on the other hand, have the freedom to choose their WSP based on the price offered. It is found that in such a greedy and non-cooperative behavioral game model, it is in the best interest of the WSPs to adhere to a price threshold which is a consequence of a price (Nash) equilibrium. We conducted extensive simulation experiments, the results of which show that the proposed auction model entices WSPs to participate in the auction, makes optimal use of the common spectrum pool, and avoids collusion among WSPs. We also demonstrate how pricing can be used as an effective tool for providing incentives to the WSPs to upgrade their network resources and offer better services.

In many markets, efficiency depends on the quality of information that participants have. However, participants may face frictions in accessing information, which could result in significant welfare losses. My dissertation studies the causes and consequences of information frictions, focusing on the security analyst market and the public cloud market. The first two chapters investigate how information frictions are generated in the security analyst market. Security analysts observe signals and compete to make forecasts on securities’ earnings, which serve as public information to investors. Here I study how analysts’ incentives affect the quality of information they provide. In Chapter 1, I consider security analysts’ incentives as a whole and estimate them using revealed preference. Security analysts are rewarded for being more accurate than their peers. This reward for relative accuracy leads analysts to distort their forecasts to differentiate themselves, but also disciplines them from being overoptimistic. I structurally estimate a contest model with incomplete information to capture both effects, disentangling the payoffs for relative accuracy, optimism and absolute accuracy. Using the model, I conduct counterfactuals to evaluate policies that reduce analysts’ payoff for relative accuracy. I simulate the effect of these policies on the quality of information in terms of forecast errors and variances. The reward for relative accuracy reduces errors by 33 - 58%, but increases variances by 4%. It is optimal to have moderate competition between the covering analysts of each security. In Chapter 2, I ask where these incentives come from. Are analysts motivated by dynamic incentives of reputation, or by short-term compensation such as bonuses? I show with reduced form evidence that low-reputation analysts may face more incentive to outperform their rivals than high reputation analysts. Building on this, I develop and estimate a dynamic model where analysts compete to build reputation and earn compensation. I find that analysts face a strong reputation-building incentive because high reputation is associated with a much higher fixed wage. Meanwhile, their forecasts have an insignificant impact on their immediate compensation. Chapter 3 studies the consequences of information frictions in the public cloud market. Firms need information about available technologies to make good adoption decisions. Inattentiveness to such information may create stickiness to outdated technology. In a joint project with Sida Peng and Peichun Wang, we study the welfare benefits of firms’ public cloud adoption and the consequence of consumer inertia in this market. We develop a novel demand model that allows for both multiple product choices and continuous quantities on each product. We estimate the model using a proprietary dataset on individual firms’ cloud usage history from a major public cloud provider. The estimated average return on investment in cloud is 2.2 times the cost of investment, which is driven by smaller firms disproportionately benefiting from access to computing resources on the cloud. On the other hand, inertia on the cloud leads to sub-optimal product choices for all firms and reduces welfare from cloud usage by almost 62%. We show that introductory discounts incentivizing firms to try new products can improve both consumer welfare and provider revenue.

This research focuses on estimating the shift in consumer willingness to pay (WTP) a price premium/discount for Native warm season grassed beef and for Bermuda grassed beef, an exotic species to the U.S. We utilized sensory analysis and the Becker-Degroot-Marschak (BDM) mechanism to elicit consumers’ preference for four different types of beef to determine how the premiums/discounts consumers place on these differentiated products change across information regimes: perception, experience, and complete information. Subjects participated in the BDM mechanism three times to determine their WTP: once after observing the raw packaged product with standard labeling information, once after a blind taste sample of the products, and once after complete information has been provided that links the raw packaged product to the blind taste sample. Results revealed strong preferences for NWSGed beef regardless of finishing methods, and positive impacts of the sensory results and label information on grassinished beef steaks.

The objectives of this research were to use trained sensory panels to identify key sensory profiling characteristics of freshwater prawns, develop and test price elicitation methods using the trained panels and gather freshwater prawn pricing information. Prawns were procured through a reputable producer and delivered to the Department of Food Science, Nutrition & Health Promotion at Mississippi State University. A trained sensory panel was utilized to develop descriptive terms to express key attributes of freshwater prawns. Trained panelists evaluated both raw and cooked product forms for 40 and 83 attributes, respectively. At the end of the sensory evaluation of the products, trained panelists also completed a willingness-to-pay ballot. Results determined sensory attributes for both raw and cooked prawns and the value of the raw product form.

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The changing nature of the energy grid in recent years has prompted key stakeholders to think of ways to address incoming challenges. Transactive energy is an approach that promises to dynamically align active grid elements coming up in the previously inactive consumers' side to achieve a reliable and smarter grid. This work models the distribution grid structure as a combination of microgrids. A blockchain-in-the loop simulation framework is modelled and simulated for a residential microgrid using power system simulators and transactive agents. Blockchain smart contracts are used to coordinate peer-to-peer energy transactions in the microgrid. The model is used to test three market coordination schemes: a simple auction-less scheme, an auction-less scheme with a normalized sorting metric and an hour ahead single auction scheme with penalties for unfulfilled bids. Case studies are presented of a microgrid with 30 homes, at different levels of solar and energy storage penetration within the microgrid, all equipped with responsive and unresponsive appliances and transactive agents for the HVAC systems. The auction-less scheme with a normalized sorting metric is observed to provide a fairer advantage to smaller solar installations in comparison to the simple auction-less method. It is then concluded that the auction-less schemes are most beneficial to users, as they would not need sophisticated forecasting technology to reduce penalties from bid quantity inaccuracies, as long as the energy mix within the microgrid is diverse enough. / Master of Science / The legacy energy industry involved the bulk transfer of energy from huge generation plants through long transmission lines to the end consumers. However, with the onset of improved renewable energy and information technologies, energy is now being generated closer to the consumer side with appliances capable of actively participating in the energy system now widely available. Transactive energy with blockchain has been proposed in order to dynamically coordinate these systems to work towards a more reliable and smarter grid using economic value in a transparent and secure way. This work models a transactive power grid as a combination of microgrids using a blockchain network to coordinate hourly peer-to-peer energy transactions. The blockchain-in-the-loop simulation model is used to compare three different market mechanisms in a residential microgrid of 30 homes with varying levels of solar panels, batteries and transactive thermostats installed. Two auction-less schemes - one with a normalized sorting metric - and an hour ahead single auction mechanism are analyzed. While the auction-less scheme with the normalized metric is seen to be fairer than the simple auction-less scheme, it is concluded that the auction-less schemes are most beneficial to residents. This is because sophisticated forecasting technology would not be needed like in the hour ahead auction scheme, provided that the microgrid has participants with diverse energy consumption and production profiles throughout the day.

Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. In recent decades due to increasing electricity demand, there is an increased likelihood of electrical power systems experiencing stress conditions. These conditions lead to a limited supply and cascading failures throughout the grid that could lead to wide area outages. Demand Response (DR) is a method involving the curtailment of loads during critical peak load hours, that restores that balance between demand and supply of electricity. In order to implement DR and ensure efficient energy operation of buildings, detailed energy monitoring is essential. This information can then be used for energy management, by monitoring the power consumption of devices and giving users detailed feedback at an individual device level. Based on the data from the Energy Information Administration (EIA), approximately half of all commercial buildings in the U.S. are 5,000 square feet or smaller in size, whereas the majority of the rest is made up of medium-sized commercial buildings ranging in size between 5,001 and 50,000 square feet. Given that these medium-size buildings account for a large portion of the total energy demand, these buildings are an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique to disaggregate the power of individual HVACs using machine learning classification techniques, where a single power meter is used to collect aggregated HVAC power data of a building. This method is then tested over a number of case studies, from which it is found that the aggregated power data can be disaggregated to accurately predict the power consumption and state of activity of individual HVAC loads. The second work focuses on a DR algorithm involving the determination of an optimal bid price for double auctioning between the user and the electric utility, in addition to a load scheduling algorithm that controls single floor HVAC and lighting loads in a commercial building, considering user preferences and load priorities. A number of case studies are carried out, from which it is observed that the algorithm can effectively control loads within a given demand limit, while efficiently maintaining user preferences for a number of different load configurations and scenarios. Therefore, the major contributions of this work include- A novel HVAC power disaggregation technique using machine learning methods, and also a DR algorithm for HVAC and lighting load control, incorporating user preferences and load priorities based on a double-auction approach. / Doctor of Philosophy / Electrical power systems consist of a large number of power generators connected to consumers through a complex system of transmission and distribution lines. Within the electric grid, a continuous balance between generation and consumption of electricity must be maintained., ensuring stable operation of the grid. When electricity demand is high, Demand Response (DR) is a method that can be used to reduce user loads, restoring the balance between demand and supply of electricity. Based on data from the Energy Information Administration (EIA), half of all commercial buildings in the US measure 5,000 square feet or smaller in size, whereas the majority of the other half is made up of medium-sized commercial buildings measuring in at between 5,001 to 50,000 square feet. This makes these commercial buildings an ideal target for participating in DR. In this dissertation, two broad solutions for commercial building DR have been presented. The first is a load disaggregation technique, where power consumption and activity of individual HVACs can be obtained, using a single power meter. The second work focuses on a DR algorithm, that controls single floor HVAC and lighting loads in a commercial building, based on a user generated bid price for electricity, user preferences and load priorities, when electricity demand is at its peak.

In the framework of public auctions the auction-companies work, which on the one hand face the problem of realizing the public auction from the point of view of marketing, on the other hand, at the same time, they try to be visible in the relationship the auctioneer and the auction proposer. It is a closed cycle whose task is to strengthen the marketing potential and, at the same time to increase the financial profit of the firm. The aim of this diplom thesis is focused on the advertising acivities of a smalll auction company, which at present is not very effective. This auction company has been lately rather successful and has proved its vitality, on the contrary, its profits in the form af the auctioneer´s rewards have started to stagnate. One of the reasons may be its lack of financial means which might have been released for the purpose of advertising. That is why, the aim of this diploma thesis is to avaluate the present advertising activities of an auction company. The positive and negative working of a concrete auctioneer in the public auctions market is going to be specified by help of single cases. The aim is also the identifying of the elementary mistakes giving evidences of bad advertising activities of the company. In the conclusion some recommendations concerning advertising activities are defined; their task is to enable small auction companies to make themselves more visible in the public auctions market.

Resumé AJ Bankrupt's estate is the cornerstone of bankruptcy proceedings, also known as insolvency proceedings, and serves as the initial point for this thesis. Without the knowledge of the scope of this term and its interconnection with other institutions it would not be possible to achieve one of the main aims of insolvency proceedings, the satisfaction of creditors' claims. The main focus of this work is realization of immovable property, i.e. the process of transformation of property included in the list of bankrupt's estate, during which the insolvency trustee must proceed in accordance with the instruction of the secured creditor. Ideally, security of the creditor shall be chosen from one of the methods in the enumerative list provided for by the Insolvency Act. The goal of the thesis is to define the process of realizing immovable property in a comprehensive manner, and to evaluate positives and negatives of the respective methods, including their practical aspects. The work is divided into eight chapters, which are composed to reflect the overall image of the process of realizing the bankrupt's estate, beginning with the term proper and ending with the resolution on the release of the yield of realization to the creditor. The first chapter provides a brief introduction of the issue as well as giving...